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IN the UNITED STATES DISTRICT COURT for the EASTERN DISTRICT of TEXAS MARSHALL DIVISION Plaintiff, V. TIBCO SOFTWARE, INC., Defe

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IN the UNITED STATES DISTRICT COURT for the EASTERN DISTRICT of TEXAS MARSHALL DIVISION Plaintiff, V. TIBCO SOFTWARE, INC., Defe Case 2:15-cv-01135 Document 1 Filed 06/23/15 Page 1 of 45 PageID #: 1 IN THE UNITED STATES DISTRICT COURT FOR THE EASTERN DISTRICT OF TEXAS MARSHALL DIVISION FELLOWSHIP FILTERING TECHNOLOGIES, LLC, Plaintiff, Civil Action No._________ v. JURY TRIAL DEMANDED TIBCO SOFTWARE, INC., Defendant. COMPLAINT FOR PATENT INFRINGEMENT Plaintiff Fellowship Filtering Technologies, LLC (“Fellowship Filtering” or “Plaintiff”), by and through its attorneys, brings this action and makes the following allegations of patent infringement relating to U.S. Patent No. 5,884,282 (“the ‘282 patent”). Defendant TIBCO Software, Inc. (“TIBCO” or “Defendant”) infringes Fellowship Filtering’s ‘282 patent in violation of the patent laws of the United States of America, 35 U.S.C. § 1 et seq. INTRODUCTION 1. In an effort to expand its product base and profit from the sale of infringing computer-based data analytics technologies, TIBCO has undertaken to copy the technologies and inventions of Gary Robinson, the inventor or the ‘282 patent and a co-owner of Fellowship Filtering. 2. TIBCO positions its data analytics systems as providing corporations with revolutionary mechanisms of gaining insights into customer behavior. TIBCO’s data analytics systems incorporate the inventions disclosed in Mr. Robinson’s ‘282 patent. Vivek Ranadivé, the founder of TIBCO, has described TIBCO’s use of predictive algorithms as integral to the success of TIBCO’s products. Case 2:15-cv-01135 Document 1 Filed 06/23/15 Page 2 of 45 PageID #: 2 I believe there are five forces driving this generation. First, the explosion of data that was created from the beginning of mankind to last year and you call that X then in the last year there is 10X of that data created. In fact, as you are sitting in this conference today there will be more content put up on YouTube then Hollywood has generated in its entire history. Vivek Ranadivé, TUCON 2013, INTRODUCTORY REMARKS AT TIBCO ANNUAL USER CONFERENCE, December 5, 2013, https://www.youtube.com/watch?v=UnpVDtrvj8E. 3. Mr. Ranadivé has described TIBCO’s systems that incorporate Mr. Robinson’s inventions (disclosed in the ‘282 patent) as breakthroughs, providing techniques that improve the functioning of computer systems and providing answers to business problems that would have previously been unavailable to users. Predictive Business is a whole new ball game. It is not just about collecting more data or using sophisticated analytics. Rather is about combining technologies, business techniques, and orchestration processes to better leverage your corporate assets. Vivek Ranadivé, THE POWER TO PREDICT: HOW REAL TIME BUSINESSES ANTICIPATE CUSTOMER NEEDS, CREATE OPPORTUNITIES, AND BEAT THE COMPETITION 2-3 (2006). 4. Mr. Robinson is a mathematician and inventor of computer-based recommendation engine technologies that enable the recommending of products and/or content based on novel algorithms that calculate the preferences based on the similarity and dissimilarity of users of a website. 5. Mr. Robinson studied mathematics at Bard College and New York University's Courant Institute of Mathematical Sciences. Mr. Robinson is the recipient of the National Science Foundation – SBIR award. 6. Mr. Robinson is a named inventor of numerous United States Patents. Mr. Robinson’s patents have been acquired by companies including Google, Inc. (“Google”).1 Patents referencing Mr. Robinson’s ‘282 patent have been purchased or assigned to companies including: International Business Machines Corporation (“IBM”), 2 Google,3 Amazon.com, Inc. 1 See USPTO Assignment Abstract of Title Database Reel/Frame No. 021552/0256. 2 U.S. Patent Nos. 6,356,879; 6,931,397; 7,006,990; 7,080,064; 7,099,859; 7,389,285; 7,885,962; 8,700,448; and 8,825,681. 3 U.S. Patent Nos. 7,966,632; 8,290,964; and 8,762,394. 2 Case 2:15-cv-01135 Document 1 Filed 06/23/15 Page 3 of 45 PageID #: 3 (“Amazon”),4 and Intel Corporation (“Intel”).5 ROBINSON’S LANDMARK ELECTRONIC MAIL INVENTIONS 7. The Robinson Method, named after Gary Robinson, is a Bayesian statistical approach that uses a text-classifier, rule-based method for determining the relevancy of an email message. Numerous leading SPAM filtering technologies utilize the Robinson Method.6 8. Mr. Robinson’s contributions to the field of electronic mail filtering are recognized as landmark technologies. Günther Hölbling, PERSONALIZED MEANS OF INTERACTING WITH MULTIMEDIA CONTENT 119 (2011). 9. Mr. Robinson has published academic articles on statistical approaches to identifying content. A 2003 article in Linux Journal described these mathematical approaches for identifying unsolicited bulk email. Mr. Robinson’s approach was notable because it assigned scores to both “spam” and “ham” and used an algorithm to guess intelligently whether an incoming 4 U.S. Patent Nos. 6,266,649; 7,113,917; 7,433,832; 7,478,054; 7,664,669; 7,778,890; 7,908,183; 7,921,042; 7,945,475; 8,001,003; 8,024,222; 8,108,255; 8,140391; and 8,180,689. 5 U.S. Patent Nos. 6,405,034, 7,590,415, and 7,797,343. 6 Ricardo Villamarín-Salomón & José Carlos Brustoloni, Bayesian Bot Detection Based on DNS Traffic Similarity, in SAC’09: ACM SYMPOSIUM ON APPLIED COMPUTING 2040—41 (2009); Masahiro Uemura & Toshihiro Tabata, Design and Evaluation of a Bayesian-filter-based Image Spam Filtering Method, in PROCEEDINGS OF THE 2008 INTERNATIONAL CONFERENCE ON INFORMATION SECURITY AND ASSURANCE 46-51 (2008) (“the Robinson Method”); MARCO ANTONIO BARRENO, Technical Report No. UCB/EECS-2008-63, EVALUATING THE SECURITY OF MACHINE LEARNING ALGORITHMS 45 (2008); Manabu Iwanaga et al., Evaluation of Anti-Spam Methods Combining Bayesian Filtering and Strong Challenge and Response, in PROCEEDINGS OF CNIS’03 (COMMUNICATION, NETWORK, AND INFORMATION SECURITY) 214—19 (2003); BLAINE NELSON, Technical Report No. UCB-EECS-2010-140, BEHAVIOR OF MACHINE LEARNING ALGORITHMS IN ADVERSARIAL ENVIRONMENTS 62-67 (2010); Gordon V. Cormack & Mona Mojdeh, Autonomous Personal Filtering Improves Global Spam Filter Performance, in PROCEEDINGS OF THE 6TH CONFERENCE ON EMAIL AND ANTI-SPAM 2 (2009). 3 Case 2:15-cv-01135 Document 1 Filed 06/23/15 Page 4 of 45 PageID #: 4 email was spam. This approach was incorporated in products such as SpamAssassin, which used a Bayesian statistical approach using a text-classifier rule to distinguish “spam” and “ham” messages.7 10. Mr. Robinson’s inventions relating to filtering technologies have been widely adopted by spam filters including Spam Assassin8 (PC Magazine’s Editor’s Choice for spam filtering), SpamSieve9 (MacWorld’s Software of the Year), and SpamBayes10 (PC Worlds Editor’s Choice for spam filtering). ROBINSON’S DEVELOPMENT OF CONTENT FILTERING SYSTEMS 11. Prior to developing groundbreaking electronic mail filtering technologies, Mr. Robinson used his insights to develop the automated content filtering technologies that are used today by TIBCO and many of the world’s largest corporations without attribution or compensation. 12. In the late 1980’s, Mr. Robinson developed a system for collecting preference information and providing recommendations. His company, 212-ROMANCE, was an automated, 7 Gary Robinson, A Statistical Approach to the Spam Problem, LINUX JOURNAL 107 (2003). 8 SpamAssassin Pro, in PC MAGAZINE February 25, 2003 at 82 (awarding SpamAssassin Pro its editors’ choice award); The SpamAssassin Project: Train SpamAssassin's Bayesian Classifier, http://spamassassin.apache.org/full/3.2.x/doc/sa-learn.html (“Gary Robinson's f(x) and combining algorithms, as used in SpamAssassin”); Credits - The Perl Programming Language - Algorithms, http://cpansearch.perl.org/src/JMASON/Mail-SpamAssassin-3.2.5/CREDITS (“The Bayesian-style text classifier used by SpamAssassin's BAYES rules is based on an approach outlined by Gary Robinson. Thanks, Gary!”). 9 David Progue, From the Deck of David Progue: The Follow-Up Edition, N.Y. TIMES, April 5, 2006, http://www.nytimes.com/2006/04/05/technology/06POGUE-EMAIL.html (“Spam Sieve is just incredibly, amazingly accurate; my in box is clean, baby, clean!”). 10 Tom Spring, Spam Slayer: 2003 Spam Awards, PCWORLD MAGAZINE, December 15, 2003, at 36 (“What makes the program unique is that SpamBayes doesn't use predetermined spam definitions. Rather, it constantly evolves by scanning your in-box to build custom definitions.”); MARCO ANTONIO BARRENO, Technical Report No. UCB/EECS-2008-63, EVALUATING THE SECURITY OF MACHINE LEARNING ALGORITHMS 45 (2008) (“SpamBayes classifies using token scores based on a simple model of spam status proposes by Robinson . SpamBayes Tokenizes the header and body of each email before constructing token spam scores. Robinson’s method assumes that each token’s presence of absence in an email affects that email’s spam status independently from other tokens.”). 4 Case 2:15-cv-01135 Document 1 Filed 06/23/15 Page 5 of 45 PageID #: 5 voice-based dating service that used a passive data collection process to determine likely romantic matches.11 Mr. Robinson’s contributions to the field of content filtering were pioneering. Matthew French, Romantic Beginnings Have Worldwide Effect, BOSTON BUS. J., May 20, 2002. 13. In the mid-1990s, Mr. Robinson recognized that the growing adoption of the internet and increased computational power enabled collection and processing of data relating to customer and user preferences that, with proper data analytics processes, could provide accurate recommendations of products and content. 14. Mr. Robinson further recognized that the growth of the internet led to unique problems involving information overload that filtering techniques using specific new collaborative filtering technologies could solve. 15. At the time, existing recommendation technologies, discussed in the ‘282 patent, failed to teach a robust and accurate process for providing recommendations. A key insight of Mr. Robinson was that the input of buying habits and/or ratings information from multiple users over the internet allowed similarity values among users to be calculated based on identifying subgroups of similar users.
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